docstr: :param x: the input into the bijection
  :type x: torch.Tensor
  
  Invokes the bijection x=>y; in the prototypical context of a
  :class:`~pyro.distributions.TransformedDistribution` `x` is a sample from the base distribution (or the output
  of a previous transform)
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code: def _call(self, x):
      """
      :param x: the input into the bijection
      :type x: torch.Tensor
  
      Invokes the bijection x=>y; in the prototypical context of a
      :class:`~pyro.distributions.TransformedDistribution` `x` is a sample from the base distribution (or the output
      of a previous transform)
      """
      x0, alpha_prime, beta_prime = (
          self._params() if callable(self._params) else self._params
      )
  
      # Ensure invertibility using approach in appendix A.2
      alpha = F.softplus(alpha_prime)
      beta = -alpha + F.softplus(beta_prime)
  
      # Compute y and logDet using Equation 14.
      diff = x - x0
      r = diff.norm(dim=-1, keepdim=True)
      h = (alpha + r).reciprocal()
      h_prime = -(h**2)
      beta_h = beta * h
  
      self._cached_logDetJ = (
          (x0.size(-1) - 1) * torch.log1p(beta_h)
          + torch.log1p(beta_h + beta * h_prime * r)
      ).sum(-1)
      return x + beta_h * diff
  
imports: import math
  from functools import partial
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.distributions import Transform, constraints
  from pyro.nn import DenseNN
  from ..conditional import ConditionalTransformModule
  from ..torch_transform import TransformModule
  from ..util import copy_docs_from
label_desc: Use image node_id 3 for calling the ConditionedRadial obj's underlying member method code with example usage: obj._call(x) and returns: unknown